考虑火电与储能的广域分层协调精细化规划方法

A Wide-Area Hierarchical Coordination and Refined Planning Method Considering Thermal Power and Energy Storage

中文关键词:火电灵活性改造  储能规划  联合规划  广域协调  清洁低碳

keywords:thermal power flexibility transformation  energy storage planning  joint planning  wide-area coordination  clean and low-carbon

中文摘要:针对新型电力系统低碳化转型背景下传统火储联合规划方法难以兼顾电网拓扑约束与机组级运行特性导致的大规模新能源弃电问题，提出了考虑火电与储能的广域分层协调精细化规划模型。首先, 构建了基于节点链接矩阵的潮流分层计算模型，借助阻抗矩阵映射与机组启、停动态关联算法，实现节点层级下火电机组差异化调峰特性与储能充、放电策略的协同解析；其次，建立多目标优化模型，以系统全生命周期成本最小化为核心目标，同时兼顾碳排放约束、供电可靠性及新能源消纳需求，形成“火电-抽水蓄能-电化学储能”多类型资源协同规划框架。最后，以某省实际电网为实证对象，设计多场景对比实验。实验结果表明，所提方法可降低系统总成本 6.27%、提升新能源消纳率 21.75%、减少碳排放 8.6 万t，验证了其在破解“源-网-储”协同规划难题中的有效性。

Abstract:Against the backdrop of the low-carbon transition of the new-type power system，traditional combined thermal power and energy storage planning methods struggle to balance power grid topology constraints and unit-level operational characteristics， resulting in large-scale curtailment of new energy. To address this issue，a wide-area hierarchical coordinated refined planning model considering thermal power and energy storage is proposed. Firstly， a hierarchical power flow calculation model based on the node-link matrix is constructed. By leveraging impedance matrix mapping and the dynamic correlation algorithm for unit startup and shutdown， the collaborative analysis of the differentiated peak-shaving characteristics of thermal power units and the charging/discharging strategies of energy storage at the node level is realized. Secondly，a multi-objective optimization model is established， with the minimization of the system’s full-life-cycle cost as the core objective， while incorporating carbon emission constraints， power supply reliability，and new energy consumption requirements， thus forming a coordinated planning framework for multiple types of resources including “thermal power-pumped storage-electrochemical energy storage”. Finally，taking the actual power grid of a province as the empirical object， multi-scenario comparative experiments are designed. The experimental results show that the proposed method can reduce the total system cost by 6.27%， improve the new energy consumption rate by 21.75%， and reduce carbon emissions by 86，000 tons， verifying its effectiveness in solving the collaborative planning problem of “source-grid-storage”.